Multipass approach for performing channel equalization training

ABSTRACT

A method comprises performing a first pass test over a plurality of sets of equalization coefficients to filter the plurality of sets of equalization coefficients to produce one or more filtered sets of equalization coefficients. Each filtered set of equalization coefficients meets a first predetermined threshold. The method also comprises performing a second pass test over the one or more filtered sets of equalization coefficients to determine a final set of equalization coefficients that meets a second predetermined threshold. The second pass test produces more accurate results than the first pass test.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to data transmissions, and, morespecifically, to an approach for performing channel equalizationtraining.

2. Description of the Related Art

A typical data connector, such as a peripheral component interface (PCI)or PCI express (PCIe), allows different processing units within acomputer system to exchange data with one another. For example, aconventional computer system could include a central processing unit(CPU) that exchanges data with a graphics processing unit (GPU) across aPCIe bus.

When a signal is transmitted across the data connector on a transmissionchannel, some frequency components may be attenuated more than others,which can make the signal illegible at the receiving end. Astransmission speeds get faster, the transmissions can become more proneto errors as the noise effects are more severe. In high-speedtransmission channels, the signal quality is critically important. Onetechnique to combat this tendency is to “equalize” the channel so thatthe frequency domain attributes of the signal at the input end arefaithfully reproduced at the output end, resulting in fewer errors.High-speed serial communications protocols like PCIe use equalizers toprepare data signals for transmission.

Equalization can be performed on both the transmit end and the receiveend of a channel. For transmit equalization, the signal can be reshapedat the transmit end before the signal is sent to attempt to overcome thedistortion that will be introduced by the channel. At the receive end,the signal can be reconditioned to improve the signal quality.

For transmit equalization in PCIe, two parameters known as equalizationcoefficients can be used to tune the transmitter. A typical system mayhave hundreds of combinations of equalization coefficients, and some ofthese combinations will produce better equalization results than others.The signal quality is critically important in high-speed transmissionchannels, so an optimal set of coefficients is crucial to ensureaccurate transmissions. During the equalization process, one combinationof coefficients must be selected that meets the performance requirementsof the system. In addition, selecting this combination must be donewithin a fixed time limit so that the system can boot up or begin otherprocesses. Testing every combination of coefficients to find the bestone is unfeasible, as this approach will usually take too much time.Additionally, current approaches used to test a subset of combinationsof coefficients will often lead to selecting a suboptimal combination.

Accordingly, what is needed in the art is a technique that tests andselects equalization coefficients for a high-speed bus in a moreefficient manner.

SUMMARY OF THE INVENTION

One embodiment of the present invention sets forth a method foranalyzing equalization coefficients for a high-speed data bus. Themethod includes performing a first pass test over a plurality of sets ofequalization coefficients to filter the plurality of sets ofequalization coefficients to produce one or more filtered sets ofequalization coefficients. Each filtered set of equalizationcoefficients meets a first predetermined threshold. The method alsoincludes performing a second pass test over the one or more filteredsets of equalization coefficients to determine a final set ofequalization coefficients that meets a second predetermined threshold.The second pass test produces more accurate results than the first passtest.

Advantageously, selecting equalization coefficients using the abovetechniques allows for faster selection of coefficients that meet thequality criteria required by the system.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the present invention;

FIG. 2 is a block diagram of a parallel processing subsystem for thecomputer system of FIG. 1, according to one embodiment of the presentinvention;

FIG. 3 is a conventional illustration of transmitted signals andreceived signals;

FIG. 4 is a conventional illustration of transmitted data and receiveddata that results in errors;

FIG. 5 is an illustration of signals at the receive end before and afterequalization according to one embodiment of the present invention;

FIG. 6 is a conventional illustration of an eye diagram from anoscilloscope;

FIG. 7 is an illustration of a map of equalization coefficientsaccording to one embodiment of the present invention;

FIGS. 8A and 8B illustrate one technique for performing a coarse grainsearch on a map of equalization coefficients according to one embodimentof the present invention;

FIG. 9 is an illustration of another technique for performing a coarsegrain search on a map of equalization coefficients according to oneembodiment of the present invention;

FIG. 10 is an illustration of a technique for performing a fine grainsearch on a map of equalization coefficients according to one embodimentof the present invention;

FIG. 11 is a flowchart illustrating an example multipass approach forchannel equalization training according to one embodiment of the presentinvention;

FIG. 12 is a flowchart illustrating an example technique for performinga coarse grain search according to one embodiment of the presentinvention;

FIG. 13 is a flowchart illustrating another example technique forperforming a coarse grain search according to one embodiment of thepresent invention; and

FIG. 14 is a flowchart illustrating an example technique for performinga fine grain search according to one embodiment of the presentinvention.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a more thorough understanding of the present invention. However,it will be apparent to one of skill in the art that the presentinvention may be practiced without one or more of these specificdetails. In other instances, well-known features have not been describedin order to avoid obscuring the present invention.

System Overview

FIG. 1 is a block diagram illustrating a computer system 100 configuredto implement one or more aspects of the present invention. Computersystem 100 includes a central processing unit (CPU) 102 and a systemmemory 104 that includes a device driver 103. CPU 102 and system memory104 communicate via an interconnection path that may include a memorybridge 105. Memory bridge 105, which may be, e.g., a Northbridge chip,is connected via a bus or other communication path 106 (e.g., aHyperTransport link) to an input/output (I/O) bridge 107. I/O bridge107, which may be, e.g., a Southbridge chip, receives user input fromone or more user input devices 108 (e.g., keyboard, mouse) and forwardsthe input to CPU 102 via path 106 and memory bridge 105. A parallelprocessing subsystem 112 is coupled to memory bridge 105 via a bus orother communication path 113 (e.g., a peripheral component interconnect(PCI) express, Accelerated Graphics Port (AGP), or HyperTransport link);in one embodiment parallel processing subsystem 112 is a graphicssubsystem that delivers pixels to a display device 110 (e.g., aconventional cathode ray tube (CRT) or liquid crystal display (LCD)based monitor). A system disk 114 is also connected to I/O bridge 107. Aswitch 116 provides connections between I/O bridge 107 and othercomponents such as a network adapter 118 and various add-in cards 120and 121. Other components (not explicitly shown), including universalserial bus (USB) or other port connections, compact disc (CD) drives,digital video disc (DVD) drives, film recording devices, and the like,may also be connected to I/O bridge 107. Communication pathsinterconnecting the various components in FIG. 1 may be implementedusing any suitable protocols, such as PCI, PCI Express (PCIe), AGP,HyperTransport, or any other bus or point-to-point communicationprotocol(s), and connections between different devices may use differentprotocols as is known in the art.

PPU 112 is configured to execute a software application, such as e.g.device driver 103, that allows PPU 112 to generate arbitrary packettypes that can be transmitted across communication path 113. Thosepacket types are specified by the communication protocol used bycommunication path 113. In situations where a new packet type isintroduced into the communication protocol (e.g., due to an enhancementto the communication protocol), PPU 112 can be configured to generatepackets based on the new packet type and to exchange data with CPU 102(or other processing units) across communication path 113 using the newpacket type.

In one embodiment, the parallel processing subsystem 112 incorporatescircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the parallel processing subsystem 112incorporates circuitry optimized for general purpose processing, whilepreserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, the parallelprocessing subsystem 112 may be integrated with one or more other systemelements, such as the memory bridge 105, CPU 102, and I/O bridge 107 toform a system on chip (SoC).

It will be appreciated that the system shown herein is illustrative andthat variations and modifications are possible. The connection topology,including the number and arrangement of bridges, the number of CPUs 102,and the number of parallel processing subsystems 112, may be modified asdesired. For instance, in some embodiments, system memory 104 isconnected to CPU 102 directly rather than through a bridge, and otherdevices communicate with system memory 104 via memory bridge 105 and CPU102. In other alternative topologies, parallel processing subsystem 112is connected to I/O bridge 107 or directly to CPU 102, rather than tomemory bridge 105. In still other embodiments, I/O bridge 107 and memorybridge 105 might be integrated into a single chip. Large embodiments mayinclude two or more CPUs 102 and two or more parallel processing systems112. The particular components shown herein are optional; for instance,any number of add-in cards or peripheral devices might be supported. Insome embodiments, switch 116 is eliminated, and network adapter 118 andadd-in cards 120, 121 connect directly to I/O bridge 107.

FIG. 2 illustrates a parallel processing subsystem 112, according to oneembodiment of the present invention. As shown, parallel processingsubsystem 112 includes one or more parallel processing units (PPUs) 202,each of which is coupled to a local parallel processing (PP) memory 204.In general, a parallel processing subsystem includes a number U of PPUs,where U≧1. (Herein, multiple instances of like objects are denoted withreference numbers identifying the object and parenthetical numbersidentifying the instance where needed.) PPUs 202 and parallel processingmemories 204 may be implemented using one or more integrated circuitdevices, such as programmable processors, application specificintegrated circuits (ASICs), or memory devices, or in any othertechnically feasible fashion.

Referring again to FIG. 1, in some embodiments, some or all of PPUs 202in parallel processing subsystem 112 are graphics processors withrendering pipelines that can be configured to perform various tasksrelated to generating pixel data from graphics data supplied by CPU 102and/or system memory 104 via memory bridge 105 and bus 113, interactingwith local parallel processing memory 204 (which can be used as graphicsmemory including, e.g., a conventional frame buffer) to store and updatepixel data, delivering pixel data to display device 110, and the like.In some embodiments, parallel processing subsystem 112 may include oneor more PPUs 202 that operate as graphics processors and one or moreother PPUs 202 that are used for general-purpose computations. The PPUsmay be identical or different, and each PPU may have its own dedicatedparallel processing memory device(s) or no dedicated parallel processingmemory device(s). One or more PPUs 202 may output data to display device110 or each PPU 202 may output data to one or more display devices 110.

In operation, CPU 102 is the master processor of computer system 100,controlling and coordinating operations of other system components. Inparticular, CPU 102 issues commands that control the operation of PPUs202. In some embodiments, CPU 102 writes a stream of commands for eachPPU 202 to a pushbuffer (not explicitly shown in either FIG. 1 or FIG.2) that may be located in system memory 104, parallel processing memory204, or another storage location accessible to both CPU 102 and PPU 202.PPU 202 reads the command stream from the pushbuffer and then executescommands asynchronously relative to the operation of CPU 102.

Referring back now to FIG. 2, each PPU 202 includes an I/O unit 205 thatcommunicates with the rest of computer system 100 via communication path113, which connects to memory bridge 105 (or, in one alternativeembodiment, directly to CPU 102). The connection of PPU 202 to the restof computer system 100 may also be varied. In some embodiments, parallelprocessing subsystem 112 is implemented as an add-in card that can beinserted into an expansion slot of computer system 100. In otherembodiments, a PPU 202 can be integrated on a single chip with a busbridge, such as memory bridge 105 or I/O bridge 107. In still otherembodiments, some or all elements of PPU 202 may be integrated on asingle chip with CPU 102.

In one embodiment, communication path 113 is a PCIe link, in whichdedicated lanes are allocated to each PPU 202, as is known in the art.Other communication paths may also be used. As mentioned above, thecontraflow interconnect may also be used to implement the communicationpath 113, as well as any other communication path within the computersystem 100, CPU 102, or PPU 202. An I/O unit 205 generates packets (orother signals) for transmission on communication path 113 and alsoreceives all incoming packets (or other signals) from communication path113, directing the incoming packets to appropriate components of PPU202. For example, commands related to processing tasks may be directedto a host interface 206, while commands related to memory operations(e.g., reading from or writing to parallel processing memory 204) may bedirected to a memory crossbar unit 210. Host interface 206 reads eachpushbuffer and outputs the work specified by the pushbuffer to a frontend 212.

Each PPU 202 advantageously implements a highly parallel processingarchitecture. As shown in detail, PPU 202(0) includes a processingcluster array 230 that includes a number C of general processingclusters (GPCs) 208, where C≧1. Each GPC 208 is capable of executing alarge number (e.g., hundreds or thousands) of threads concurrently,where each thread is an instance of a program. In various applications,different GPCs 208 may be allocated for processing different types ofprograms or for performing different types of computations. For example,in a graphics application, a first set of GPCs 208 may be allocated toperform tessellation operations and to produce primitive topologies forpatches, and a second set of GPCs 208 may be allocated to performtessellation shading to evaluate patch parameters for the primitivetopologies and to determine vertex positions and other per-vertexattributes. The allocation of GPCs 208 may vary dependent on theworkload arising for each type of program or computation.

GPCs 208 receive processing tasks to be executed via a work distributionunit 200, which receives commands defining processing tasks from frontend unit 212. Processing tasks include indices of data to be processed,e.g., surface (patch) data, primitive data, vertex data, and/or pixeldata, as well as state parameters and commands defining how the data isto be processed (e.g., what program is to be executed). Workdistribution unit 200 may be configured to fetch the indicescorresponding to the tasks, or work distribution unit 200 may receivethe indices from front end 212. Front end 212 ensures that GPCs 208 areconfigured to a valid state before the processing specified by thepushbuffers is initiated.

When PPU 202 is used for graphics processing, for example, theprocessing workload for each patch is divided into approximately equalsized tasks to enable distribution of the tessellation processing tomultiple GPCs 208. A work distribution unit 200 may be configured toproduce tasks at a frequency capable of providing tasks to multiple GPCs208 for processing. By contrast, in conventional systems, processing istypically performed by a single processing engine, while the otherprocessing engines remain idle, waiting for the single processing engineto complete its tasks before beginning their processing tasks. In someembodiments of the present invention, portions of GPCs 208 areconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading in screen space to produce a rendered image. Intermediatedata produced by GPCs 208 may be stored in buffers to allow theintermediate data to be transmitted between GPCs 208 for furtherprocessing.

Memory interface 214 includes a number D of partition units 215 that areeach directly coupled to a portion of parallel processing memory 204,where D≧1. As shown, the number of partition units 215 generally equalsthe number of DRAM 220. In other embodiments, the number of partitionunits 215 may not equal the number of memory devices. Persons skilled inthe art will appreciate that dynamic random access memories (DRAMs) 220may be replaced with other suitable storage devices and can be ofgenerally conventional design. A detailed description is thereforeomitted. Render targets, such as frame buffers or texture maps may bestored across DRAMs 220, allowing partition units 215 to write portionsof each render target in parallel to efficiently use the availablebandwidth of parallel processing memory 204.

Any one of GPCs 208 may process data to be written to any of the DRAMs220 within parallel processing memory 204. Crossbar unit 210 isconfigured to route the output of each GPC 208 to the input of anypartition unit 215 or to another GPC 208 for further processing. GPCs208 communicate with memory interface 214 through crossbar unit 210 toread from or write to various external memory devices. In oneembodiment, crossbar unit 210 has a connection to memory interface 214to communicate with I/O unit 205, as well as a connection to localparallel processing memory 204, thereby enabling the processing coreswithin the different GPCs 208 to communicate with system memory 104 orother memory that is not local to PPU 202. In the embodiment shown inFIG. 2, crossbar unit 210 is directly connected with I/O unit 205.Crossbar unit 210 may use virtual channels to separate traffic streamsbetween the GPCs 208 and partition units 215.

Again, GPCs 208 can be programmed to execute processing tasks relatingto a wide variety of applications, including but not limited to, linearand nonlinear data transforms, filtering of video and/or audio data,modeling operations (e.g., applying laws of physics to determineposition, velocity and other attributes of objects), image renderingoperations (e.g., tessellation shader, vertex shader, geometry shader,and/or pixel shader programs), and so on. PPUs 202 may transfer datafrom system memory 104 and/or local parallel processing memories 204into internal (on-chip) memory, process the data, and write result databack to system memory 104 and/or local parallel processing memories 204,where such data can be accessed by other system components, includingCPU 102 or another parallel processing subsystem 112.

A PPU 202 may be provided with any amount of local parallel processingmemory 204, including no local memory, and may use local memory andsystem memory in any combination. For instance, a PPU 202 can be agraphics processor in a unified memory architecture (UMA) embodiment. Insuch embodiments, little or no dedicated graphics (parallel processing)memory would be provided, and PPU 202 would use system memoryexclusively or almost exclusively. In UMA embodiments, a PPU 202 may beintegrated into a bridge chip or processor chip or provided as adiscrete chip with a high-speed link (e.g., PCIe) connecting the PPU 202to system memory via a bridge chip or other communication means.

As noted above, any number of PPUs 202 can be included in a parallelprocessing subsystem 112. For instance, multiple PPUs 202 can beprovided on a single add-in card, or multiple add-in cards can beconnected to communication path 113, or one or more of PPUs 202 can beintegrated into a bridge chip. PPUs 202 in a multi-PPU system may beidentical to or different from one another. For instance, different PPUs202 might have different numbers of processing cores, different amountsof local parallel processing memory, and so on. Where multiple PPUs 202are present, those PPUs may be operated in parallel to process data at ahigher throughput than is possible with a single PPU 202. Systemsincorporating one or more PPUs 202 may be implemented in a variety ofconfigurations and form factors, including desktop, laptop, or handheldpersonal computers, servers, workstations, game consoles, embeddedsystems, and the like.

Equalization Using Variable Length Training Evaluation Periods

FIG. 3 is a conventional illustration of transmitted signals andreceived signals. In a computer system, signals can be transmitted inthe forms of 1s and 0s along wires to their destination. The signals candegrade as they travel over a channel, so at the destination it may behard to determine whether the received bit is a 1 or a 0. Input signal10 represents a data 1 at the transmission end of a channel. Inputsignal 10 comprises a pulse width of approximately T_(b). Input signal12 represents a data 0 at the transmission end of the channel. Inputsignal 12 also comprises a pulse width of approximately T_(b). Inputsignals 10 and 12 appear sharp, like step functions.

The right side of FIG. 3 illustrates two output signals at the receiveend of the channel. Output signal 20 comprises a received data 1, andoutput signal 22 comprises a received data 0. These output signals aredistorted due to Intersymbol Interference (ISI) and thus the outputsignals do not exactly match the sharp look of input signals 10 and 12.If the distortion becomes too great at the receive end, a 1 could bemistaken for a 0 (or vice versa) and an error would be introduced in thetransmission.

FIG. 4 is a conventional illustration of transmitted data and receiveddata that results in errors. Transmit data at a channel input isrepresented by waveform 30. Waveform 30 comprises a series of 1s and 0stransmitted on a channel. Waveform 30 appears sharp, with cleartransitions between 1s and 0s. The dotted line represents a slice level,which marks the boundary between a data 1 and a data 0. Waveform 40 is arepresentation of the signal at the receive end. Because of interferencein the channel, the received signal can be distorted and does not appearas sharp as the transmitted data represented by waveform 30. Waveform 50is a representation of the data in waveform 40. In other words, whenwaveform 40 is converted to 1s and 0s the result is waveform 50. Asshown in FIG. 4, waveform 50 (the received data) does not exactly matchwaveform 30 (the transmitted data). Errors were introduced in thetransmission. An errored zero 42 is shown, which means a 0 wastransmitted but the receiver received a 1. An errored one 44 is alsoshown, where a 1 was transmitted but the receiver received a 0. Whendata switches quickly between 1s and 0s, as shown in waveform 30, someof the transitions may be lost due to interference in the receivedsignal (waveform 40), resulting in errors (such as errors 42 and 44) inthe received data (waveform 50). As data connections and datatransmissions become faster, these types of errors become more commonbecause the received signals do not have enough time to fully transitionfrom a 1 to a 0 or vice versa. Equalization can be performed to helpprevent these errors.

In PCI Express Gen 3, equalization comprises implementing settings thatcompensate for ISI to make the received signal look like the originaltransmitted signal. Both transmission and receive equalization may beperformed. In transmission equalization, the signal at the transmit endis “reshaped” before the signal is sent, in a manner that iscomplementary to the distortion that will be introduced by the channel.In other words, the reshaping can counteract the distortion. Reshapingcan allow the receive end to more easily differentiate between 1s and0s. In receive equalization, the signal is reconditioned at the receiveend to counter the distortion introduced by the channel and furtherimprove the signal quality. In PCI Express Gen 3, both transmission andreceive equalization can be performed.

FIG. 5 illustrates one example of signals at the receive end of atransmission before and after equalization according to one embodimentof the present invention. The signals shown in waveform 60 have not hadequalization performed on them. The signals shown in waveform 62illustrate signals after equalization. The transitions between 1s and 0scan more be seen more clearly in waveform 62 than in waveform 60 due toequalization.

FIG. 6 is a conventional illustration of an eye diagram 70 from anoscilloscope probed at the receive end of a transmission channel.Measurement #5 is the eye height. The eye height provides a measure ofthe noise or interference in the channel. As the interference increases,the “eye” appears to close and the eye height measurement becomessmaller. As the interference decreases, the eye height measurementbecomes larger. Thus the eye height can be measured to check the qualityof the transmissions on a channel. During an equalization trainingprocess, multiple sets of parameters can each be tested using an eyediagram to find an optimal set of parameters. As is well understood,other measurements shown in the eye diagram 70 (measurements 1-4 and6-9) can be used to derive other performance measures of the channel.

Below is a discussion of a number of example equalization algorithms.These algorithms may be used to perform equalization within given timelimits. Modifications and changes may be made to these algorithmswithout departing from the broader spirit and scope of the invention.

Equalization in PCIe can be accomplished by finding optimal transmissionequalization coefficients that can be used to tune the transmitter.Three coefficients (known as the precursor, the postcursor, and themaincursor) can be specified, and the coefficients are constrained bythe following equations:

precursor+maincursor+postcursor=FS  (1)

FS−2*(precursor+postcursor)>=LF  (2)

Precursor<=FS/4  (3)

where LF=low frequency and FS=full swing, both of which are constant fora particular port. Coefficients that satisfy these equations are called“legal” and those that do not are called “illegal.” The term “optimalcoefficients” means coefficients that result in a bit error rate (BER)of 10⁻¹² or less. A typical computer system may have hundreds of legalcombinations of equalization coefficients. The algorithms and techniquesdescribed in this disclosure may be used to efficiently find a set ofoptimal coefficients among the legal combinations of coefficients.

FIG. 7 is an illustration of a map of equalization coefficientsaccording to one embodiment of the present invention. A map such as thisexample may be used to visualize coefficients. Each point on the maprepresents a combination of a precursor and a postcursor. Because themaincursor can be derived from the other two coefficients using equation(1) above, the search algorithm only tracks the precursor and thepostcursor. FIG. 7 illustrates the postcursor (C₊₁) on the x-axis andthe precursor (C⁻¹) on the y-axis. In this example, the FS value is 24and the coefficients are expressed as ratios of FS. The shaded squareson the map represent presets and the dotted lines represent theboundaries separating legal coefficients from illegal coefficients.

Each point on the map has an eye height associated with it, whichrepresents the link quality produced by the coefficients at that point.The evaluation of this link quality may be performed by a state machinesuch as a decision feedback equalizer, or DFE. Any suitable equalizermay be used, and may be referred to as EQ Train in certain exampleembodiments in this disclosure. The end result is a score, referred toas “qeye” in certain example embodiments in this disclosure. The qeye isa measure of the eye height, and higher values represent better linkqualities. The goal of the search algorithms is to find the highestvalue on the map, or alternatively to find a value that meets or exceedsa predetermined threshold. Quality metrics other than the eye height maybe used, such as a measure of the eye width or a measure of the eyearea.

As noted above, there often is not enough time to test every point onthe map. In one example implementation using PCIe, each point may takemore than 200 microseconds to evaluate. Evaluating 250 points wouldtherefore require approximately 50 milliseconds, but the PCIespecification, for example, only allows a maximum of 24 milliseconds tofind the optimal coefficients. In addition, it is desirable to finish asquickly as possible so that other operations can be performed. Thesearch algorithms described here will work for short channels (where alot of points on the map are acceptable) and long channels (where only afew points on the map are acceptable). The search algorithms describedhere can also take advantage of monotonically increasing qeye values inlocal areas of the map to conduct a more efficient search.

In one embodiment of this invention, a coarse grain search is conductedover a large coefficient map to find a region of the map that is likelyto have the highest qeye. Once that analysis is complete, a fine grainsearch can be performed on a smaller portion of the map to find thehighest qeye. Performing the coarse grain search provides an overview ofthe map in a short amount of time, and then the more accurate fine grainsearch can be used to hone in on a set of optimal coefficients.

The coarse grain search can be conducted in a variety of ways. FIGS. 8Aand 8B illustrate one example embodiment of a coarse grain search. Asshown, the coarse grain search comprises four sequences—each of whichiteratively “walks” the map using a step size of 2. The first sequence,illustrated in FIG. 8A, starts at coordinates (0,0) on the map, whilethe second, third, and fourth sequences start at (1,1), (0,1), and(1,0), respectively. The first sequence covers the shaded points on themap in the following order: (0,0), (0,2), (0, 4), (0,6), (2,6), (2,4),(2,2), (2,0), (4,0), (4,2), (4,4), (6,2), (6,0), (8,0). FIG. 8Billustrates the second sequence, which begins at (1,1) and walks the mapwith a step size of 2 as shown. Sequences three and four (not shown) arealso run. By breaking down the coarse grain search into four sequences,the algorithm is able to cover a wide area of the map during the earlyphase of the search, and then cover the entire map if time permits. Ifthe coarse grain search runs out of time before all four sequences arecompleted, the best point found up to that point in time is stored andpassed on to the fine grain search.

Exit conditions may be put into place for the coarse grain search. Inone example embodiment, the coarse grain search algorithm can exit ifany of the following events occur:

-   -   (1) a point has been found that exceeds the minimum acceptable        qeye threshold;    -   (2) the number of iterations has exceeded a predetermined value;    -   (3) the total time in the coarse grain search has exceeded a        predetermined value; or    -   (4) all the points specified in all the selected coarse grain        sequences have been tested.

Once the exit condition occurs, the best point on the map is used as thestarting point for the fine grain search.

FIG. 9 is an illustration of another technique for performing a coarsegrain search on a map of equalization coefficients according to oneembodiment of the present invention. In this example embodiment, thecoarse grain search splits each axis into 2, 4, or 8 parts to divide thecoefficient map into approximately equal regions. Then the center pointof each region is selected and the coefficients associated with thosecenter points are checked for legality. The qeye for each legal point isdetermined, and the point with the highest qeye is selected as the “bestcoarse point” and used as the starting point for the fine grain search.

In some embodiments, techniques other than a coarse grain search may beused to select a starting point for the fine grain search. The use ofthese techniques may be faster than performing a coarse grain search.For example, a software program or process may be used to specify oneset of coefficients to evaluate. The specified set of coefficients maybe a set of coefficients that was previously used for equalization inthe system. In some closed systems, such as laptop computers, theoptimal equalization coefficients may not change during each boot cycle,so these coefficients can be used as a starting point for the fine grainsearch or can even be re-used without a fine grain search if the eyeheight associated with the coefficients meets an acceptable threshold.

FIG. 10 is an illustration of a technique for performing a fine grainsearch on a map of equalization coefficients according to one embodimentof the present invention. The coarse grain search algorithms describedabove are used to find a “best coarse point” to be used as the startingpoint for the fine grain search. One example algorithm for a fine grainsearch will now be described. Each set of coefficients evaluated iscalled a “tuning attempt” or “iteration” of the algorithm. The finegrain search algorithm begins by evaluating the neighboring coefficientsaround the starting point. Then, the algorithm “walks” one point at atime in the direction of the neighboring point with the highest eyeheight. The adjacent, or neighboring, point associated with the greatesteye height can be referred to as a currently traversed point. Walking inthat direction (a first direction) and evaluating each point along theway continues until the eye height begins to fall, or until a boundaryis reached. At the point on the map where the eye height begins to fall(or a boundary is reached), the algorithm evaluates the eightsurrounding points on the map (or fewer, in the case of a boundary). Thealgorithm then again walks in the direction of the point with thehighest eye height (a second direction), and repeats these walking andexploring steps until an exit condition is met. Four potential exitconditions are:

-   -   (1) all eight surrounding points are lower—the algorithm has        reached a local maximum;    -   (2) time runs out;    -   (3) the number of iterations has reached a predetermined        threshold; or    -   (4) the eye height reaches an acceptable threshold.

Again, FIG. 10 provides a detailed example of a fine grain search.Assume that the coarse grain search selected point (3,5) as the startingpoint. The numbers on the map in FIG. 10 are exemplary only, and areused to represent the eye height for the equalization coefficientsassociated with each point on the map. The number at starting point(3,5) is 5. The algorithm evaluates the neighboring coefficients aroundthis starting point, and finds that point (4,5) has the highest eyeheight of all the neighboring points—an eye height of 6. Evaluating theneighboring points in this manner can be referred to as the “ExploreMode.” The algorithm then walks in the direction of point (4,5), onestep at a time, until the eye height begins to fall. This direction canbe considered a first direction, and point (4,5) can be referred to asthe currently traversed point. Walking along the map can be referred toas the “Walk Mode.” At point (8,5) the eye height is 10, and at point(9,5) the eye height is 9. The algorithm will therefore re-center aroundthe point where the eye height beings to fall, point (8,5). Thealgorithm then evaluates the eight points surrounding point (8,5) on themap and finds that point (8,6), with an eye height of 12, is the pointwith the highest eye height. So the algorithm walks from point (8,5) inthe direction of point (8,6) (i.e., a second direction), and continuesuntil the eye height begins to fall. The eye height is 19 at point(8,10) and falls at point (8,11) to 14. Once again, the algorithmre-centers, this time around the point (8,10) and evaluates the eightpoints surrounding point (8,10). After this evaluation, the algorithmfinds that the eye height of all eight surrounding points is lower thanthe eye height at point (8,10). Therefore, the algorithm has met an exitcondition and the equalization coefficients at point (8,10) are selectedfor use in the system. The fine grain search in this example isconcluded.

During a fine grain search, each set of coefficients on the map that areencountered by the algorithm can first be checked for legality. If anillegal point is encountered while in Explore Mode, the algorithm canskip that point and move on. If an illegal point is encountered while inWalk Mode, the algorithm can stop walking at that point and switch toExplore Mode.

The coarse grain searches and fine grain searches described above can beimplemented in a variety of ways and in a variety of systems. Thesetechniques can be used, for example, with high speed serialcommunications protocols like PCIe to prepare data signals fortransmission. The PCIe 3.0 Base Specification does not addressequalization search algorithms.

In one example implementation involving PCIe 3.0, Recovery.Equalizationis a substate of the Recovery state of the Link Training and StatusState Machine (LTSSM). This substate is used to find the optimaltransmit coefficients for proper operation at 8.0 GT/s speed. Theequalization coefficients can be determined automatically by hardwareusing a handshake protocol. Therefore a Recovery.Equalization trainingalgorithm is used to find the optimal coefficients in the shortestpossible time.

The Recovery.Equalization substate is further divided into fivesub-substates:

-   -   (1) Recovery.Equalization Phase 0    -   (2) Recovery.Equalization Phase 1    -   (3) Recovery.Equalization Phase 2 Req Coeff    -   (4) Recovery.Equalization Phase 2 EQ Train    -   (5) Recovery.Equalization Phase 3

Recovery.Equalization Phase 0 is reserved for upstream ports only (i.e.endpoint). When the downstream port (i.e. rootport) requests entry intoRecovery.Equalization, the endpoint enters this state first. Prior toentering this state, the endpoint applies the rootport suggestedequalization coefficients to its transmitter (the rootport suggestedpreset is communicated by the rootport at gen1/gen2 speed just beforethe first speed change to gen3). The reason for a rootport suggestedpreset is that since most of the trace is on the motherboard, therootport manufacturer would be in a better position to predict what theoptimal preset would be, so the manufacturer should specify the presetto start with. This manufacturer specification may allow the searchalgorithm to settle at the optimal point sooner.

Recovery.Equalization Phase 1 is for both upstream and downstream ports.Both ports apply the rootport suggested presets to their transmitters.The ports also communicate their FS and LF values to each other viatraining sets (TS1s), so that each port can plug these values into theconstraint equations specified above. This step enables each port tosearch only legal coefficients in its respective “master” phase (i.e.,phase 2 for upstream ports, phase 3 for downstream ports).

In Recovery.Equalization Phase 2 Req Coeff, the upstream port (endpoint)sends requests to the downstream port (rootport) to set its Tx(transmit) settings to the values that the upstream port thinks would beoptimal, and waits for the request to be accepted or rejected. Theupstream port can take care to request only legal coefficients, but mayalso have a mechanism to handle the case where legal coefficients arerejected by the other side. The upstream port may request equalizationsettings to be applied by specifying the individual coefficients(precursor, maincursor, postcursor).

In Recovery.Equalization Phase 2 EQ Train, the upstream port evaluatesthe link quality produced by the coefficients requested inRecovery.Equalization Phase 2 Req Coeff. This evaluation can beperformed by an equalizer, and the end result is the qeye. The port thenmakes a note of this value, and compares it with the qeye values seen inearlier requests—if this is the highest one so far, the port stores therequested coefficients in a temporary variable, such as (best_precursor,best_maincursor, best_postcursor).

The next state is Recovery. Equalization Phase 2 Req Coeff if morecoefficients need to be tried (to search for an even higher qeye), orRecovery.Equalization Phase 3, if the max qeye is greater than theacceptable threshold (or if the algorithm cannot find any other pointsworth trying). The maximum time that can be spent iterating betweenRecovery.Equalization Phase2 Req Coeff and Recovery.Equalization Phase 2EQ Train is 24 ms in this embodiment. After 24 ms is up, the porttransitions to Recovery.Equalization Phase 3.

In Recovery.Equalization Phase 3, the downstream port makes requests tothe upstream port to change the upstream port's Tx settings, and thedownstream port tries to find the best qeye for the link in the upstreamport-to-downstream port direction. This phase has a timeout of 32 ms.The upstream port simply has to receive the requests, reflect them backand indicate “accepted” when they are legal (or “reject” when they areillegal), and apply the legal ones to its own transmitter settings.

The Recovery.Equalization Training Algorithm is only applicable to thesub-substates Recovery.Equalization Phase 2 Req Coeff andRecovery.Equalization Phase 2 EQ Train, since only those states areallocated to the endpoint for performing the coefficient search. Eachset of coefficients tried is called a ‘tuning attempt’ or ‘iteration’ ofthe algorithm. The transition from Recovery.Equalization Phase 2 RequestCoeff can happen when certain conditions are met. The transition fromRecovery.Equalization Phase 2 EQ Train to Recovery.Equalization Phase 2Req Coeff can occur when an EQ Train signal goes high.

One example embodiment of a search algorithm encompassing both coarseand fine approaches, as well as other approaches, is described below.This example embodiment can be broadly classified into fivesub-algorithms:

-   -   (1) Request Previous Best Settings—if the “best coefficient”        variables have valid values, then request these values again.        This sub-algorithm is useful if the link enters the        Recovery.Equalization state again and the user wants to test the        point found by the previous equalization search.    -   (2) SBIOS Preset Request—request the preset that was recommended        by the rootport prior to entering gen3.    -   (3) Software Specified Coefficient Request—software supplies a        set of coefficients that may be optimal via registers    -   (4) Coarse Grain Algorithm—search the coefficient space at a        certain stride (or step_size) to find a region of high qeye        values; or perform any other type of coarse grain algorithm, as        described above    -   (5) Fine Grain Algorithm—search near the highest point uncovered        in the previous steps, but restrict the step_size to 1. Always        advance in the direction that has the highest gradient of        increase in qeye, as described above.    -   (6) A last step is to re-request the best point found in the        previous 5 steps. This step is a one-iteration pass through        step.

FIG. 11 is a flow diagram of method steps for performing multipasschannel equalization training according to one embodiment of the presentinvention. Although the method steps are described in conjunction withFIGS. 1, 2, and 7-10, persons skilled in the art will understand thatany system configured to perform the method steps, in any order, fallswithin the scope of the present invention. Processing unit 102 isconfigured to perform the various steps of the method 1100 whenexecuting a software application stored in a memory, such as systemmemory 104. In some embodiments, parallel processing subsystem 112 mayperform some of the steps of the method 1100.

As shown, a method 1100 begins in step 1110, where processing unit 102performs a first pass test over a set of equalization coefficients. Thefirst pass test produces one or more filtered sets of equalizationcoefficients that each meets a certain criteria. The first pass test maycomprise one of the coarse grain search algorithms as described above.The first pass test may, for example, determine an eye height associatedwith each set of equalization coefficients and filter the sets ofequalization coefficients based at least in part on that eye height.

In step 1120, processing unit 102 performs a second pass test over theone or more filtered sets of equalization coefficients to determine afinal set of equalization coefficients. The second pass test producesmore accurate results than the first pass test. The second pass test maytake a longer amount of time to perform than the first pas test. Inaddition, the second pass test may comprise the fine grain searchalgorithm as described above. The second pass test may, for example,determine an eye height associated with each set of equalizationcoefficients and select or reject the set of equalization coefficientsbased at least in part on that eye height.

FIG. 12 is a flow diagram of method steps for performing a coarse grainsearch according to one embodiment of the present invention. Althoughthe method steps are described in conjunction with FIGS. 1, 2, and 7-10,persons skilled in the art will understand that any system configured toperform the method steps, in any order, falls within the scope of thepresent invention. Processing unit 102 is configured to perform thevarious steps of the method 1200 when executing a software applicationstored in a memory, such as system memory 104. In some embodiments,parallel processing subsystem 112 may perform some of the steps of themethod 1200.

The process begins with step 1210. Processor 102 executes the softwareapplication to select a starting point on a map of equalizationcoefficients. In an exemplary embodiment illustrated in FIG. 8A, thestarting point that is selected is (0,0). Other points may be selectedin other embodiments.

In step 1220, processor 102 executes the software application to walkiteratively along the map using a step size greater than one. In theexemplary embodiment illustrated in FIG. 8A, the step size is 2. Otherstep sizes may be used in other embodiments. In the exemplaryembodiment, the technique walks from point (0,0) to point (0,2), then topoints (0,4), (0,6), (2,6), etc., as described above with respect toFIG. 8A. This method can cover a wide area of the map in a short amountof time by using a step size greater than one.

In step 1230, processor 102 executes the software application to measurethe eye height of each point. The point with the highest eye height isstored in memory, and other points may be stored as well. Any suitableprocess for measuring eye height may be used. The point with the highesteye height may be used as the optimal coefficients or may be used as astarting point for another search algorithm, such as a fine grainsearch.

In step 1240, processor 102 executes the software application to exitthe method if an exit condition occurs. A number of potential exitconditions may be used. A first exit condition occurs if a point hasbeen found that exceeds the minimum acceptable qeye threshold. If thisexit condition occurs, the search has found a set of optimalcoefficients and these coefficients may be selected and utilized fortransmissions. A second exit condition occurs if the number ofiterations has exceeded a predetermined value. The search may beperformed with a predetermined maximum number of iterations so as to notexceed a time limit for the coarse grain search. If the number ofiterations is reached, the search process can exit. Another exitcondition may occur if the total time in the coarse grain search hasexceeded a predetermined value. A fourth exit condition may occur if allthe points specified in all the selected coarse grain sequences havebeen tested. When an exit condition occurs, processor 102 may executethe software application to select the best equalization coefficientsfound (as measured by eye height) and either use those coefficient forequalization or use those coefficients as the starting point for a finegrain search.

In step 1250, if no exit condition has occurred, processor 102 executesthe software application to repeat the method by starting at step 1210and using a different starting point. As an exemplary embodiment, theprocess may use starting point (0,0) for the first sequence and then usestarting point (0,1) for a second sequence. The second sequence proceedsexactly as the first by walking along the map using a step size greaterthan one, measuring eye heights, and exiting if an exit conditionoccurs. Any number of additional sequences can occur if the exitconditions have not been met.

FIG. 13 is a flow diagram of method steps for performing a coarse grainsearch according to one embodiment of the present invention. Althoughthe method steps are described in conjunction with FIGS. 1, 2, and 7-10,persons skilled in the art will understand that any system configured toperform the method steps, in any order, falls within the scope of thepresent invention. Processing unit 102 is configured to perform thevarious steps of the method 1300 when executing a software applicationstored in a memory, such as system memory 104. In some embodiments,parallel processing subsystem 112 may perform some of the steps of themethod 1300.

The process begins with step 1310. In this step, processor 102 executesthe software application to split a map of equalization coefficientsinto a plurality of regions. An example of splitting a map in thismanner can be seen in FIG. 9. The map can be split into regions ofapproximately equal size or into regions of unequal size. The map can besplit into any appropriate number of regions.

In step 1320, processor 102 executes the software application to measurethe eye height of one point in each region. The point that is measuredin each region can be selected in a variety of ways. In someembodiments, the center point of the region could be selected. In otherembodiments, a random or pseudorandom point could be selected. The eyeheights are measured using any suitable technique.

In step 1330, processor 102 executes the software application to selectthe point with the highest eye height as the starting point for a finegrain search algorithm or another search algorithm. Ideally, selectingthe point with the highest eye height as the starting point leads tofinding an optimal set of equalization coefficients more quickly thanstarting with another point.

FIG. 14 is a flow diagram of method steps for performing a fine grainsearch according to one embodiment of the present invention. Althoughthe method steps are described in conjunction with FIGS. 1, 2, and 7-10,persons skilled in the art will understand that any system configured toperform the method steps, in any order, falls within the scope of thepresent invention. Processing unit 102 is configured to perform thevarious steps of the method 1400 when executing a software applicationstored in a memory, such as system memory 104. In some embodiments,parallel processing subsystem 112 may perform some of the steps of themethod 1400.

The technique begins with step 1410. In step 1410, processing unit 102selects a starting point on a map of equalization coefficients. The mapof equalization coefficients may be similar to the map shown in FIG. 7in one example embodiment. The starting point can be selected by anysuitable technique, including one of the coarse grain search algorithmsor one of the sub-algorithms described above.

In step 1420, processing unit 102 executes the software application tomeasure the eye heights of the starting point and each point adjacent tothe starting point. These eye heights can then be used to search for anoptimal set of equalization coefficients on the map. Any suitableapproach can be used to measure the eye heights.

In step 1430, processor 102 executes the software application to walkalong the map of equalization coefficients in the direction of the pointadjacent to the starting point with the highest eye height. This processwas discussed above with respect to FIG. 10.

In step 1440, processor 102 executes the software application todetermine if the eye height is still increasing or not. If the eyeheight is still increasing, the method returns to step 1430 andcontinues to walk in the direction the point with the highest eyeheight. Steps 1430 and 1440 are repeated as long as the eye height isincreasing (or until certain exit conditions are met, as discussedbelow). If the eye height is not still increasing, the method proceedsto step 1450.

In step 1450, processor 102 executes the software application to selectthe point from which the eye height begins to fall and measure the eyeheight of each adjacent point. This process was also discussed abovewith respect to FIG. 7. These eye heights are used to determine a newdirection to walk in or to determine if an exit condition is met. Theprocess then continues to step 1460.

In step 1460, processor 102 executes the software application todetermine if an exit condition has been met. A number of potential exitconditions may be used, as described above with respect to FIG. 10. Ifeach adjacent point has a lower eye height than the point where the eyeheight begins to fall, the method has found a local maximum and this mayend the fine grain search algorithm in certain embodiments. Another exitcondition may be if a predetermined time threshold has been met. A thirdexit condition may be if the eye height is above a predeterminedthreshold. If so, that eye height meets the requirements of the systemand those coefficients can be selected by processor 102 for use. Afourth exit condition may be met when the number of iterations of thealgorithm has reached a certain threshold. If an exit condition has notbeen met, the method returns to step 1430 to continue walking in thedirection of the point with the highest eye height. If an exit conditionhas been met, the method continues to step 1470.

In step 1470, processor 102 executes the software application to selectthe coefficients with the highest eye height among all the sets ofcoefficients that have been analyzed. At least one of the exitconditions described above in step 1460 has been met, and the system cannow continue with other boot-up processes. Ideally, the equalizationcoefficients that are selected will reduce the error rate in transmittedsignals.

In sum, a two-pass approach may be used to find and select a set ofoptimal equalization coefficients for high-speed data transmissions. Thefirst pass comprises a coarse grain search over a map that includespoints comprising a set of equalization coefficients at each point. Thegoal of the coarse grain search is to choose a set of equalizationcoefficients that can be used for high-speed data transmissions or thatcan be used as the starting point for a second pass. The coarse grainsearch can be performed in a variety of ways, including dividing thecoefficient map into a number of regions and testing one point in eachregion or walking iteratively around the map and testing a subset of thepoints on the map. The testing that is performed can include measuringan eye height associated with each set of equalization coefficients. Thesecond pass comprises a fine grain search over the map. The fine grainsearch involves measuring the eye height of a starting point and the eyeheight of each point adjacent to the starting point. The searchalgorithm then walks along the map in the direction of the point withthe highest eye height, and continues walking as long as the eye heightkeeps increasing. If the eye height beings to fall, the algorithmre-centers on the point with the highest eye height and evaluates alladjacent points. The algorithm then again walks in the direction of thepoint with highest eye height and continues the fine grain search untilan exit condition is met. Once an exit condition is met, the algorithmselects the equalization coefficients with the highest eye height anduses those coefficients for equalization of transmissions along thehigh-speed data connection.

Advantageously, selecting equalization coefficients using the abovetechniques allows for faster selection of coefficients that meet thequality criteria required by the system. In some systems, such asservers, the channel is long and can be extremely noisy. The subset ofacceptable coefficients is therefore very small. The search algorithmsdescribed above are effective in finding optimal coefficients by firstidentifying a region of the map of equalization coefficients whereoptimal coefficients may lie, and then by performing a thorough searchof that region to find the best coefficients. In short channel systems(such as desktop computers), a large number of points may satisfy theoperating requirements. The algorithms described herein can quickly do acoarse grain search and then locate an optimal point on the map using afine grain search. The fine grain search can walk in any one of eightpossible directions and this helps to find an optimal point quickly. Inaddition, some systems such as laptop computers have embedded links thatare unchangeable, which means the equalization coefficients are notexpected to change either. This disclosure provides for software thatdirectly assigns known quality equalization coefficients as a startingpoint for a search. A search that begins on or near a quality point canoften be completed very quickly.

Other advantages include the ability to fine tune the search algorithmsdiscussed above by altering step sizes, exit conditions, or othervariables. The algorithms discussed above can handle both legal andillegal points. The time allotted for the coarse grain search and/or thefine grain search can also be adjusted.

One embodiment of the invention may be implemented as a program productfor use with a computer system. The program(s) of the program productdefine functions of the embodiments (including the techniques describedherein) and can be contained on a variety of computer-readable storagemedia. Illustrative computer-readable storage media include, but are notlimited to: (i) non-writable storage media (e.g., read-only memorydevices within a computer such as CD-ROM disks readable by a CD-ROMdrive, flash memory, ROM chips or any type of solid-state non-volatilesemiconductor memory) on which information is permanently stored; and(ii) writable storage media (e.g., floppy disks within a diskette driveor hard-disk drive or any type of solid-state random-accesssemiconductor memory) on which alterable information is stored.

The invention has been described above with reference to specificembodiments. Persons skilled in the art, however, will understand thatvarious modifications and changes may be made thereto without departingfrom the broader spirit and scope of the invention as set forth in theappended claims. The foregoing description and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

The invention claimed is:
 1. A computer-implemented method for analyzingequalization coefficients for a high-speed data bus, the methodcomprising: performing a first pass test over a plurality of sets ofequalization coefficients to filter the plurality of sets ofequalization coefficients to produce one or more filtered sets ofequalization coefficients, wherein each filtered set of equalizationcoefficients meets a first predetermined threshold; performing a secondpass test over the one or more filtered sets of equalizationcoefficients to determine a final set of equalization coefficients thatmeets a second predetermined threshold, wherein the second pass testproduces more accurate results than the first pass test.
 2. The methodof claim 1, wherein the plurality of sets of equalization coefficientsare distributed over a map of equalization coefficients comprising aplurality of points, wherein each point on the map represents a singleset of equalization coefficients.
 3. The method of claim 2, whereinperforming the first pass test comprises splitting the map ofequalization coefficients into a plurality of regions, and testing aselected set of equalization coefficients in each region to determine ifthe selected set of equalization coefficients meets the firstpredetermined threshold.
 4. The method of claim 3, wherein testing theselected set of equalization coefficients comprises measuring an eyeheight associated with a data transmission tuned with the selected setof equalization coefficients.
 5. The method of claim 2, whereinperforming the first pass test comprises iteratively walking along themap of equalization coefficients to at least one new point, and testingthe set of equalization coefficients represented by the at least one newpoint.
 6. The method of claim 5, wherein the first pass test continuesuntil an exit condition is met.
 7. The method of claim 6, wherein theexit condition comprises: measuring an eye height associated with theset of equalization coefficients at each visited point and exiting ifthe eye height is above a predetermined threshold; iteratively walkingalong the points on the map of equalization coefficients until apredetermined number of points has been visited; exceeding apredetermined time limit for the first pass test; or completing one ormore predetermined sequences of iterative walking along the map ofequalization coefficients.
 8. The method of claim 2, wherein performingthe second pass test comprises walking along the points on the map ofequalization coefficients from a point that represents one of the one ormore filtered sets of equalization coefficients to at least one newpoint, and testing the set of equalization coefficients represented bythe at least one new point.
 9. The method of claim 8, wherein testingthe set of equalization coefficients comprises measuring an eye heightassociated with a data transmission tuned with the selected set ofequalization coefficients.
 10. A non-transitory computer-readable mediumthat, when executed by a processing unit, causes the processing unit toanalyzing equalization coefficients for a high-speed data bus, byperforming the steps of: performing a first pass test over a pluralityof sets of equalization coefficients to filter the plurality of sets ofequalization coefficients to produce one or more filtered sets ofequalization coefficients, wherein each filtered set of equalizationcoefficients meets a first predetermined threshold; performing a secondpass test over the one or more filtered sets of equalizationcoefficients to determine a final set of equalization coefficients thatmeets a second predetermined threshold, wherein the second pass testproduces more accurate results than the first pass test.
 11. Thenon-transitory computer-readable medium of claim 10, wherein theplurality of sets of equalization coefficients are distributed over amap of equalization coefficients comprising a plurality of points,wherein each point on the map represents a single set of equalizationcoefficients.
 12. The non-transitory computer-readable medium of claim11, wherein the step of performing the first pass test comprisessplitting the map of equalization coefficients into a plurality ofregions, and testing a selected set of equalization coefficients in eachregion to determine if the selected set of equalization coefficientsmeets the first predetermined threshold.
 13. The non-transitorycomputer-readable medium of claim 12, wherein the step of testing theselected set of equalization coefficients comprises measuring an eyeheight associated with a data transmission tuned with the selected setof equalization coefficients.
 14. The non-transitory computer-readablemedium of claim 11, wherein the step of performing the first pass testcomprises iteratively walking along the map of equalization coefficientsto at least one new point, and testing the set of equalizationcoefficients represented by the at least one new point.
 15. Thenon-transitory computer-readable medium of claim 14, wherein the firstpass test continues until an exit condition is met.
 16. Thenon-transitory computer-readable medium of claim 15, wherein the exitcondition comprises: measuring an eye height associated with the set ofequalization coefficients at each visited point and exiting if the eyeheight is above a predetermined threshold; iteratively walking along thepoints on the map of equalization coefficients until a predeterminednumber of points has been visited; exceeding a predetermined time limitfor the first pass test; or completing one or more predeterminedsequences of iterative walking along the map of equalizationcoefficients.
 17. The non-transitory computer-readable medium of claim11, wherein the step of performing the second pass test compriseswalking along the points on the map of equalization coefficients from apoint that represents one of the one or more filtered sets ofequalization coefficients to at least one new point, and testing the setof equalization coefficients represented by the at least one new point.18. The non-transitory computer-readable medium of claim 17, wherein thestep of testing the set of equalization coefficients comprises measuringan eye height associated with a data transmission tuned with theselected set of equalization coefficients.
 19. A computing device,including: a processor; and a memory coupled to the processor andincluding a software application that, when executed by the processor,causes the processor to: perform a first pass test over a plurality ofsets of equalization coefficients to filter the plurality of sets ofequalization coefficients to produce one or more filtered sets ofequalization coefficients, wherein each filtered set of equalizationcoefficients meets a first predetermined threshold; perform a secondpass test over the one or more filtered sets of equalizationcoefficients to determine a final set of equalization coefficients thatmeets a second predetermined threshold, wherein the second pass testproduces more accurate results than the first pass test.
 20. Thecomputing device of claim 19, wherein the plurality of sets ofequalization coefficients are distributed over a map of equalizationcoefficients comprising a plurality of points, wherein each point on themap represents a single set of equalization coefficients.